Efficient Creation of Jettability Diagrams Using Active Machine Learning.

ABSTRACT: The ability to jet a wide variety of materials consistently from print heads remains a key technical challenge for inkjet-based additive manufacturing processes. Drop watching is the most direct method for testing new inks and print head designs but such experiments are also resource consuming. In this work, a data-efficient machine learning technique called active learning is used to construct detailed jettability diagrams that identify complex regions corresponding to "no jetting," "jetting," and "desired jetting," rather than only individually sampled points. Crucially, our active learning method has resolved challenges with model selection that previously limited the accuracy of active learning in practical settings with very small experimental budgets. In addition

SUBMITTER: Pardakhti M 

PROVIDER: S-EPMC11443117 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature